scholarly journals Comparison of Fault-Tree Models for Fault Detection, Isolation, and Recovery Algorithms

2017 ◽  
Vol 14 (9) ◽  
pp. 517-522 ◽  
Author(s):  
Tszhim J. Leung ◽  
Jason H. Rife ◽  
Peter Seiler ◽  
Raghu Venkataraman
Keyword(s):  
Fault Detection ◽  
Fault Tree ◽  
Tree Models ◽  
Recovery Algorithms

U.S. Nuclear Power Plant Performance Assessment Using the Versatile Economic Risk Tool (VERT)

2021 ◽  
Author(s):  
Jaden C. Miller ◽  
Spencer C. Ercanbrack ◽  
Chad L. Pope
Keyword(s):  
Risk Assessment ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Fault Tree ◽  
Plant Performance ◽  
Economic Risk ◽  
Reliability Data ◽  
Water Reactor ◽  
Tree Models

Abstract This paper addresses the use of a new nuclear power plant performance risk analysis tool. The new tool is called Versatile Economic Risk Tool (VERT). VERT couples Idaho National Laboratory’s SAPHIRE and RAVEN software packages. SAPHIRE is traditionally used for performing probabilistic risk assessment and RAVEN is a multi-purpose uncertainty quantification, regression analysis, probabilistic risk assessment, data analysis and model optimization software framework. Using fault tree models, degradation models, reliability data, and economic information, VERT can assess relative system performance risks as a function of time. Risk can be quantified in megawatt hours (MWh) which can be converted to dollars. To demonstrate the value of VERT, generic pressurized water reactor and boiling water reactor fault tree models were developed along with time dependent reliability data to investigate the plant systems, structures, and components that impacted performance from the year 1980 to 2020. The results confirm the overall notion that US nuclear power plant industry operational performance has been improving since 1980. More importantly, the results identify equipment that negatively or positively impact performance. Thus, using VERT, individual plant operators can target systems, structures, and components that merit greater attention from a performance perspective.

scholarly journals Timed Fault Tree Models of the China Yongwen Railway Accident

2014 ◽  
Author(s):  
Yu Lu ◽  
Zhaoguang Peng ◽  
Alice Miller ◽  
Tingdi Zhao ◽  
Chris Johnson
Keyword(s):  
Fault Tree ◽  
Tree Models

Research of Fault Detection System Combining with Fault Tree Analysis with Artificial Neural Network in Large Scale Die-Forging Press

2011 ◽  
Vol 317-319 ◽  
pp. 661-666 ◽  
Author(s):  
Ji Dong Yang ◽  
Yan Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Network Model ◽  
Large Scale ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Tree Analysis ◽  
Artificial Neural ◽  
Forging Press

This paper research on a new fault detection method used in large scale die-forging press, based on the national major science and technology project (2009ZX04005-011). This method combines fault tree analysis (FTA) with artificial neural network (ANN), it need to pick up training samples of ANN from fault tree constructing by analyzing system first. An effective network model can be achieved through using BP algorithm to train the samples based on the tree-layer feed-forward network structure. This network model can output fault reasons according to fault symptoms.

Accurate reliability analysis method for quantum-dot cellular automata circuits

2015 ◽  
Vol 29 (29) ◽  
pp. 1550203
Author(s):  
Huanqing Cui ◽  
Li Cai ◽  
Sen Wang ◽  
Xiaoqiang Liu ◽  
Xiaokuo Yang
Keyword(s):  
Cellular Automata ◽  
Quantum Dot ◽  
Fault Tree ◽  
The Novel ◽  
Binary Decision ◽  
Quantum Dot Cellular Automata ◽  
Input Signals ◽  
Tree Models ◽  
The Impact ◽  
Qca Circuits

Probabilistic transfer matrix (PTM) is a widely used model in the reliability research of circuits. However, PTM model cannot reflect the impact of input signals on reliability, so it does not completely conform to the mechanism of the novel field-coupled nanoelectronic device which is called quantum-dot cellular automata (QCA). It is difficult to get accurate results when PTM model is used to analyze the reliability of QCA circuits. To solve this problem, we present the fault tree models of QCA fundamental devices according to different input signals. After that, the binary decision diagram (BDD) is used to quantitatively investigate the reliability of two QCA XOR gates depending on the presented models. By employing the fault tree models, the impact of input signals on reliability can be identified clearly and the crucial components of a circuit can be found out precisely based on the importance values (IVs) of components. So this method is contributive to the construction of reliable QCA circuits.

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